Large Scale Spatio Temporal

Large-scale spatio-temporal data analysis focuses on understanding and predicting phenomena that evolve both spatially and temporally, using massive datasets from diverse sources like sensors and remote sensing. Current research emphasizes developing robust and interpretable deep learning models, including transformer-based architectures and graph convolutional recurrent networks, to handle the complexities of these data, often addressing challenges like missing values and non-stationarity. This field is crucial for improving predictions in various domains, such as traffic management, environmental monitoring, and urban planning, by enabling more accurate and efficient decision-making based on data-driven insights.